A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Using webcrawling of publicly available websites to assess E-commerce relationships

We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

Closed-loop feedback computation model of dynamical reputation based on the local trust evaluation in business-to-consumer e-commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce

DEVELOPING AND VALIDATING A QUALITY ASSESSMENT SCALE FOR WEB PORTALS

The Web portals business model has spread rapidly over the last few years. Despite this, there have been very few scholarly findings about which services and characteristics make a Web site a portal and which dimensions determine the customers’ evaluation of the portal’s quality. Taking the example of financial portals, the authors develop a theoretical framework of the Web portal quality construct by determining the number and nature of corresponding dimensions, which are: security and trust, basic services quality, cross-buying services quality, added values, transaction support and relationship quality. To measure the six portal quality dimensions, multi item measurement scales are developed and validated.Construct Validation, Customer Retention, E-Banking, E- Loyalty, Service Quality, Web Portals

European Digital Libraries: Web Security Vulnerabilities

Purpose – The purpose of this paper is to investigate the web vulnerability challenges at European library web sites and how these issues can affect the data protection of their patrons. Design/methodology/approach – A web vulnerability testing tool was used to analyze 80 European library sites in four countries to determine how many security vulnerabilities each had and what were the most common types of problems. Findings – Analysis results from surveying the libraries show the majority have serious security flaws in their web applications. The research shows that despite country-specific laws mandating secure sites, system librarians have not implemented appropriate measures to secure their online information systems. Research limitations/implications – Further research on library vulnerability throughout the world can be taken to educate librarians in other countries of the serious nature of protecting their systems. Practical implications – The findings serve to remind librarians of the complexity in providing a secure online environment for their patrons and that a disregard or lack of awareness of securing systems could lead to serious vulnerabilities of the patrons' personal data and systems. Lack of consumer trust may result in a decreased use of online commerce and have serious repercussions for the municipal libraries. Several concrete examples of methods to improve security are provided. Originality/value – The paper serves as a current paper on data security issues at Western European municipal library web sites. It serves as a useful summary regarding technical and managerial measures librarians can take to mitigate inadequacies in their security implementation